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Currents of the single-electron transistors driven by time-dependent fields via external dissipative circuits are investigated theoretically. By expressing the external circuit in terms of driven harmonic oscillators and using the reduced-density operator method, we derive time-and environmentdependent tunneling rates in the regime of sequential tunneling and present expressions for both displacement and tunneling currents with these tunneling rates. It is found that the dissipative environments affect tunneling currents in two ways; the determination of driving voltages at tunneling junctions and the depletion of particle-hole distribution functions. Considering a simple dissipative circuit, we discuss the effects of the environment on tunneling currents in both static and time-dependent cases.

We propose and implement a promising fabrication technology for geometrically well-defined single-electron transistors based on a silicon-on-insulator quantum wire and side-wall depletion gates. The 30-nm-wide silicon quantum wire is defined by a combination of conventional photolithography and process technology, called a side-wall patterning method, and depletion gates for two tunnel junctions are formed by the doped polycrystalline silicon sidewall. The good uniformity of the wire suppresses unexpected potential barriers. The fabricated device shows clear single-electron tunneling phenomena by an electrostatically defined single island at liquid nitrogen temperature and insensitivity of the Coulomb oscillation period to gate bias conditions.

Nowadays, the importance and utilization of spatial information are recognized. Particularly in urban areas, the demand for indoor spatial information draws attention and most commonly requires high-precision 3D data. However accurate, most methodologies present problems in construction cost and ease of updating. Images are accessible and are useful to express indoor space, but pixel data cannot be applied directly to provide indoor services. A network-based topological data gives information about the spatial relationships of the spaces depicted by the image, as well as enables recognition of these spaces and the objects contained within. In this paper, we present a data fusion methodology between image data and a network-based topological data, without the need for data conversion, use of a reference data, or a separate data model. Using the concept of a Spatial Extended Point (SEP), we implement this methodology to establish a correspondence between omnidirectional images and IndoorGML data to provide an indoor spatial service. The proposed algorithm used position information identified by a user in the image to define a 3D region to be used to distinguish correspondence with the IndoorGML and indoor POI data. We experiment with a corridor-type indoor space and construct an indoor navigation platform.

Geospatial datasets are currently constructed, managed, and utilized individually according to the spatial scale of the real world, such as the ground/surface/underground or indoor/outdoor, as well the particular purpose of the geospatial data used for location-based services. In addition, LBS applications use an optimal data model and data format according to their particular purpose, and thus, various datasets exist to represent the same spatial features. Such duplicated geospatial datasets and geographical feature-based GIS data cause serious problems in the financial area, compatibility issues among LBS systems, and data integration problems among the various geospatial datasets generated independently for different systems. We propose a geospatial data fusion model called the topological relation-based data fusion model (TRDFM) using topological relations among spatial objects in order to integrate different geospatial datasets and different data formats. The proposed model is a geospatial data fusion model implemented in a spatial information application and is used to directly provide spatial information-based services without data conversion or exchange of geometric data generated by different data models. The proposed method was developed based on an extension of the AnchorNode concept of IndoorGML. The topological relationships among spatial objects are defined and described based upon the basic concept of IndoorGML. This paper describes the concept of the proposed TRDFM and shows an experimental implementation of the proposed data fusion model using commercial 3D GIS software. Finally, the limitations of this study and areas of future research are summarized.

We derive self-consistent expressions of current and noise for single-electron transistors driven by time-dependent perturbations. We take into account effects of the electrical environment, higherorder co-tunneling, and time-dependent perturbations under the two-charged state approximation using the Schwinger-Kedysh approach combined with the generating functional technique. For a given generating functional, we derive exact expressions for tunneling currents and noises and present the forms in terms of transport coefficients. It is also shown that in the adiabatic limit our results encompass previous formulas. In order to reveal effects missing in static cases, we apply the derived results to simulate realized radio-frequency single-electron transistor. It is found that photon-assisted tunneling affects largely the performance of the single-electron transistor by enhancing both responses to gate charges and current noises. On various tunneling resistances and frequencies of microwaves, the dependence of the charge sensitivity is also discussed.

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As the interest in indoor spaces increases, there is a growing need for indoor spatial applications. As these spaces grow in complexity and size, research is being carried out towards effective and efficient representation. Omnidirectional images give a snapshot of interiors and give visually rich content, but only contain pixel data. For it to be used in providing indoor services, its limitations must be overcome. First, the images must be connected to each other to represent indoor space continuously based on spatial relationships that may be provided by topological data. Second, the objects and spaces that we see in these images must also be recognized. This paper presents a study on how to link omnidirectional images and an IndoorGML data without the need for data conversion, provision of reference data, or use of different data models in order to provide Indoor Location-Based Service (LBS). We introduce the use of the Spatial Extended Point (SEP) to characterize the relationship between the omnidirectional image and the topological data. Position information of the object is used to define a region of 3D space, to determine the inclusion relationship of an IndoorGML node. We conduct an experimental implementation of the integrated data in the form of a 3D Virtual Tour. The connection of the Omnidirectional images is demonstrated by a visualization of navigation through a hallway towards a room's interior delivered to the user through a clicking action on the image.